Bayesian Statistics

Great introduction to Bayesian Statistics with some easy-enough-to-follow mathematical insights.

Great introduction to Bayesian Statistics.

Very good introduction to Bayesian Statistics.

A great introduction to Bayesian Statistics for everyone who has some basic knowledge of calculus and is familiar with the fundamentals of probability theory.

A great introduction to bayesian statistics.

However, it would have been really great if some specific examples with respect to medicine and public health practice were incorporated Excellent introduction to Bayesian statistics.

Good course as an introduction to bayesian statistics if you want to pursue more advanced courses in the field or to get some practise working with distributions under the bayesian framework.

An interesting introduction to Bayesian statistics and inference.

Great course as an introduction to Bayesian Statistics.

A Good Introduction to Bayesian Statistics.

I strongly recommend it if you want a subtle introduction to Bayesian Statistics.

Great introduction to bayesian statistics Good course.

I found the videos easy to follow and that they prepared me for the quizzes.

A very complete and easy to follow course.

Very concise and easy to follow to the end.

It was pretty intuitive and easy to follow the first couple of weeks, but then the assumed knowledge of beta and gamma distributions and their frequentist usage, stood in the way of me fully grasping the Bayesian part of it.

More 'real life' examples instead of coin flipping examples - although easy to follow - would be very helpful as well, maybe in a consecutive course with applied bayesian statistics?

This course was dense, concise, and yet easy to follow for individuals that are fairly comfortable with basic statistics.

The syllabus is easy to follow, but I also think one could benefit even more by complementing the lectures with other sources (books or other youtube explanation) It would have been great if more graphs had been provided, for easier visualization of the e.g.

Could provide more hands-on examples Super clear and easy to follow.

Professor Lee is an excellent lecturer, with a comfortable, almost conversational style that I found easy to follow and stay focused on.

Prof. Herbert Lee is a great professor providing very thorough notes and material for the Bayesian paradigm of Statistics.

Thank you so much, Herbert Lee.

Thanks to Prof Herbert Lee for making the easy to understand without sacrificing rigour.

Herbert Lee's Tests are fun (Best!)

I also really valued learning how to use R. Professor Herbert Lee is world-class.

Thanks to prof Herbert Lee and all the supporting team Learned something new :).

Herbert Lee does a very good job at building one's intuition and understanding in the general Bayesian inference.

Excellent lectures by Herbert Lee.

Thanks Prof. Herbert Lee and team.

Sometimes I needed to watch videos again because explanations were too fast for me to follow in real time, but I definitely enjoyed presentation style of Prof. Herbert Lee.

Thank you, prof. Herbert Lee, for this great course!Was able to do the course with Python instead of R, though it got a bit complicated on the last topic (regression).

Hi , this course opened a door for me in Data analysis.

Great introductory course on Bayesian data analysis.

This is a very useful course for people to do the data analysis in astronomy.

Delivers what promises: Bayesian Statistics: From Concept to Data Analysis.

The course is excellent to learn all the basic stuff needed to master the technique of Bayesian Data Analysis.

give me a new perspective on daily data analysis.

Very nice course that in my opinion nicely fits between Bolstad and Gelman in difficulty (talking in popular Bayesian Data Analysis books).

I liked the way it was taught, It's nice for who is looking for to expand data analysis.

It would have been better to have more data analysis applications Good introductory course.

This is not a tutorial on Data Analysis on R, although a short introduction is provided.

In the tech world, Machine Learning is a buzz word and Bayesian based algorithms / models are the key and this introduces one to the fundamentals of Bayesian statistics.

I took this course due to my interest in machine learning and graphical models.

I will use the principles taugh for other topics like machine learning.

I recommend this course for all data scientists and machine learning practitioners.

Also, adding modern real life examples and going into detail would make this course better A well organized course, learned important concepts in statistics and probability that will definitely help anyone wanting to specialize in machine learning or take up data science.

Followed the course in order to fill a gap I had in statistics knowledge, as I'm very interested in machine learning - deep learning, and always came upon things as MLE without really knowing well what they were talking all about.

There are books and courses out there teaching you how to use machine learning tools to solve real problems.

Besides, this is a good entry point for me to read the book "Pattern Recognition and Machine Learning".

The linear regression part could be more clear (i.e., with a lecture on the background).

For example: in the final part, under linear regression, it might be be difficult to grasp what a bayesian predictive interval means.

Everything goes smoothly, until the last section: Bayesian Linear Regression (BLE).

it was an okay course, I liked that they used R occasionally in the course, but I did not like how the concepts were discussed Overall great course, the last part (linear regression) seems somewhat disconnected from the rest of the course.

Need more information about linear regression, given material is not enough to understand topic and effectively find solution.

Quizzez are great, I spent on some quite a bit of time, but I feel they really checked if I understand the concepts and calculations.

But I do mostly feel like there is quite a bit I don't know, and while I passed, I feel like there is quite a bit more I need to do to really 'get it'.

Good real world examples and questions are posed to drive home this point at the start of the course.

Where it could have been more helpful - 1) Somewhere in between the course gets lost in math expressions and distributions drifting away from real world implications.

More real world use cases could have been there.

Was able to apply in real world.

The course was good in the sense that we could how probability distributions are used to model real world problems.Study material was certainly not adequate.

It would be better to add more explain about those equations and connect the math stuffs with the real world samples The course itself is well structured and covers a lot of material.

Actually for person like me who want to know Bayesian Statistics application in the real world and also fundamentals of it it's quite not recommended to took this lecture, honestly.

I don't find that the lectures do a good job of relating the material to real world usage.

Quiz is actually not easy just by passively viewing videos, so taking notes during lectures is strongly recommended.

I strongly recommend it.

Good course, but in my opinion misses of lectures/pdf to ease understanding.

Also, it gives some intuition for the difference between the frequentist and the bayesian approach, although that part could have been more explicit in my opinion.

Also a great thing, in my opinion, was to write the explanations on the glass instead of just displaying the final results.

the notes for the lectures are missing.In my opinion the notes, which includes the video materials could be very useful.the course was good.

I like it when the math of the subject is explained well, as done in this course, rather than "I don't want to get in to the math", or "it is beyond the scope of this course", which you often see in online courses.

Besides, some of the interesting conclusions are part of the quizzes rather than an integral part of the lectures.

However more accent should be placed on intuitive understanding rather than mathematical formalism.

Good use of R but maybe use the actual coefficient from the equations themselves rather than picking numbers pre-selected which may confuse.Unable to look at discussion forum without posting myself.

The course covers conjugate priors for several different likelihoods including the normal distribution and the binomial distribution.

The normal distribution part lacks detail.